Many recent advances in wireless transmission have rested on the use of multiple antennas for transmission and reception. Multiple antennas, fundamentally, can provide an increase in the numbers of Degrees of Freedom (DoFs) that can be exploited by a wireless system for transmission. Here, DoFs can be used to provide increased spectral efficiency (throughput) and/or added diversity (robustness). Indeed, a Single User MIMO (SU-MIMO) system with Nt transmission antennas serving a single user with Nr receive antennas may be able to exploit up to min(Nt, Nr) DoFs for downlink transmission. These DOFs, for example, can under certain conditions be used to improve throughput by a factor that grows linearly with min(Nt, Nr). Such benefits of MIMO, and increased DoFs, underlie much of the interest in using MIMO in new and future systems.
Exploiting such DoFs often requires some amount of cost to the system. One such cost is knowledge of the channel state between transmitting and receiving antennas. Such Channel State Information (CSI) often has to be available to either the transmitter (such CSI is termed CSIT) and/or to the receiver (such CSI is termed CSIR). The DoFs available also depend on having sufficient “richness” in the channels between transmitting and receiving antennas.
For example, SU-MIMO CSIR-based systems such as Bit Interleaved Coded Modulation (BICM) and D-BLAST can achieve the maximum min(Nt, Nr) DoFs under suitable channel conditions. Under such conditions, they therefore can be used to provide corresponding linear increases in spectral efficiency. Such designs are well understood by those familiar with the state of the art.
Similarly, a Multi-User MIMO (MU-MIMO) system with Nt transmission antennas at the base station and K single-antenna users (Nr=1) can provide up to min(Nt, K) DoFs. As in the case of SU-MIMO, MU-MIMO can, for example, be used to improve throughput linearly with min(Nt, K). However, unlike SU-MIMO, many MU-MIMO techniques (in fact most if not all of the prevailing MU-MIMO techniques used and studied for standards) require knowledge of CSIT. MU-MIMO based on CSIT, unlike SU-MIMO based on CSIR, requires additional overheads to estimate CSI and feedback CSI to transmitters before the transmission can even take place. Despite such overheads, MU-MIMO is of practical interest since it has the benefit over SU-MIMO of being able to grow the DoFs without having to add many receive antennas, radio frequency (RF) chains, or increase processing (e.g. decoding) complexity to portable or mobile devices.
The issue of CSI overhead has to be considered carefully. It is a fundamental issue often overlooked in assessing conventional MIMO. Such CSI-related overhead in fact can represent a fundamental “dimensionality bottleneck” that can limit the net spectral efficiency increase that can be obtained with conventional CSI-dependent MIMO.
In particular, if one wants to continue to exploit the growth in DoFs (e.g. linear growth) by increasing Nt (or Nr or K), one also has to consider how to support increased system overhead in obtaining the CSI required to formulate transmissions and decode at the receivers. Such overhead can include increased use of the wireless medium for pilots supporting CSI estimation and increased feedback between receiving and transmitting entities on such CSI estimates.
As an example, assume that for each complex scalar value that defines the CSI between a single TX antenna and a single RX antenna (this type of CSI is often termed direct CSI by some in the Standards community) a fixed percentage Fcsi of wireless-channel resources is dedicated to pilots and/or feedback. One can easily see that as the dimension of the CSI required scales with quantities like Nt, Nr and/or K, the total CSI system-related overhead grows (e.g., by Nt×Fcsi). For example, for K single antenna users, each with Nt CSI scalar terms with respect to the transmitting antenna, there are KNt such scalars. Supporting an increase in the dimension of the CSI can take more wireless-channel resources, and reduces the amount of resources left for data transmission. This overhead increase can limit continued growth in throughput if spectral efficiency improvements do not offset increased CSI overheads.
The value Fcsi is often defined either by the system or by necessity given the coherence of channels in time and/or frequency. As the state of channels changes more rapidly in time and/or frequency, more resources may need to be used to estimate and keep track of CSI.
As an example, in a Frequency Division Duplex (FDD) based 3GPP Long Term Evolution (LTE) design, 8 symbols in a resource block of 12×14 OFDM symbols are used to support downlink pilots for each of the Nt antennas. Simply considering system overheads for such pilots, and ignoring other CSI related overheads such as feedback, Fcsi≧8/168=4.76%. It means that with Nt=8, assuming the pilot structure scales linearly with additional antennas, the total CSI-overhead is at least on the order of 38%, leaving no more than 62% of symbols for supporting data transmission. Clearly, such a system would not support unbounded increases in Nt.
Thus, though symbols which carry coded data information are used more efficiently, with increased robustness and/or spectral efficiency due to the increased DoFs by MIMO, the net spectral efficiency increases has to account for the CSI overhead. Thus, the net spectral efficiency growth is in fact less than that of individual data symbols as only a fraction of no more than (1−Nt×Fcsi) of symbols can be used for data.
Recently a new class of techniques, termed “Blind Interference Alignment” (BIA) techniques, has demonstrated the ability to grow DoFs without requiring many of the CSI overheads of conventional MU-MIMO systems. It is possible for a Multi-User MIMO (MU-MIMO) system with Nt transmission antennas at the basestation and K single active-antenna users to achieve KNt/(K+Nt−1) DoFs without CSIT. Thus, as K grows, the system can approach the CSI-dependent upper bound of min(Nt,K) DoFs. This is a striking result since it goes ahead of much of the conventional thinking and conjectures over recent decades, and it provides the potential to relieve the “dimensionality bottleneck” being faced by current systems.
For such a system to work, there is a requirement that the single active-receive antenna of a user be in fact a multi-mode antenna, having a single RF chain, but able to switch between Nt modes in a pre-determined fashion. The modes must be able to create independent (e.g., linearly independent) CSI vectors for the single user. Transmission also has to be confined to a suitable coherence interval in time and frequency over which the CSI in a given mode, though unknown to the system, is assumed to be effectively constant and different from mode to mode.
The BIA technique works by creating a suitable antenna mode switching and combined data transmission vector over the K information bearing streams that are to be sent to the K users (one stream carries the intended information for one user). Such information bearing stream themselves are vectors. These are sent in various arithmetic combinations simultaneously thus using the extra DoFs provided by the antenna mode switching.
The coordination of user receive-antenna switching modes and the way the information streams are sent by the BIA scheme is designed to maximize the DoFs by complying with the following principles:                Any Nt dimensional symbol intended for a given user is transmitted through Nt slots.        During these Nt slots, the antenna-switching pattern of that user ensures that the user observes that symbol through all its Nt antenna modes (thereby in an Nt dimensional space) and can thus decode it.        In contrast, the antenna-switch patterns of the rest of the users are such that the transmission of this Nt dimensional symbol only casts an 1-dimensional shadow to their receivers. This is accomplished by ensuring that each of these receivers uses the same antenna mode in all the Nt dimensional symbol is transmitted.        
Thus, a total of (Nt+K−1) receiver dimensions are needed per user to decode Nt scalar symbols. As a result, with this scheme, K users decode a total of KNt symbols (Nt each) per (Nt+K−1) channel uses, thereby achieving the maximum possible BIA DoF of KNt/(Nt+K−1).
BIA techniques do have some inherent challenges and limitations in the scenarios in which they can be used. The first inherent problem is that they often require high Signal to Noise Ratios (SNRs) to operate effectively, e.g. the original BIA scheme may require up to 20 dB of SNR. This is due to a property of the interference alignment process which results in noise being amplified in the resulting interference-aligned streams. As a consequence of this, the original BIA technique has limited application to many users in a cellular environment. For example cell-edge users that often experience Signal to Interference plus Noise Ratios (SINRs) on the order of 0 dB or less. Note, the interference coming from interfering cells not serving the K users, thus making it for the purpose of analysis effectively noise. Many users, not just cell-edge users, do not have SINRs on the order of 20 dB or more. Unfortunately, it is such lower SNR users that are often the ones that need techniques to help them boost their spectral efficiency. High SNR users can often use simple MIMO or Single Input Single Output (SISO) techniques with satisfactory rates. The BIA scheme therefore requires modification and a proper deployment setup to enable it to be useful to many users in a cellular environment.